Explore global development with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.1.4     ✔ stringr 1.4.0
## ✔ readr   2.1.0     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

  1. Why does it make sense to have a log10 scale on x axis?
  2. Who is the outlier (the richest country in 1952 - far right on x axis)?
  • #1: It does so, since money makes more money/inflation - therefore it provides a more accurate depiction of the trend
  • #2: The answer is given in the code chunk below:
gapminder %>% 
  filter(year==1952) %>%  # We start of by filtering the data, so it only includes data from the year 1952
  group_by(country) %>% # We then group the data after country, since it is the country outlier, that we have to find
  summarize(gdpPercap) %>% # We then summarize over gdp per capita, to see the values for the different countries
  arrange(desc(gdpPercap)) %>% # We then arrange it in a descending order, so we get the richest country first, which would have to be the outlier
  head(n = 10) # We see the first 10 countries, as the addtional countries can be used for comparison to the outlier
## # A tibble: 10 × 2
##    country        gdpPercap
##    <fct>              <dbl>
##  1 Kuwait           108382.
##  2 Switzerland       14734.
##  3 United States     13990.
##  4 Canada            11367.
##  5 New Zealand       10557.
##  6 Norway            10095.
##  7 Australia         10040.
##  8 United Kingdom     9980.
##  9 Bahrain            9867.
## 10 Denmark            9692.

The richest country in 1952 was Kuwait - Kuwait is therefore the outlier

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

  1. Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)
  2. What are the five richest countries in the world in 2007?
  • #3: The answer is given in the code chunk below:
options(scipen=10000) # Eliminating the scientific notation 

ggplot(subset(gapminder, year == 2007), aes(gdpPercap,lifeExp, size = pop, color = continent)) + # We set up the ggplot object, and set 'color' to be continent, so we can differentiate the continents by color. 
  geom_point() +
  ylab("Life expectancy (years)")+ # Making the axes labels more legible
  xlab("GDP per capita (constant 2011 international $)") + 
  labs(color = "Continent", size = "Population size") + # Making the legend more legible
  scale_x_log10()+ # Scaling the x-axis to be on the log10 scale
  scale_y_continuous(n.breaks = 15) # Make some more ticks on the y-axis to make the figure more legible

  • #4: The answer is given in the code chunk below:
gapminder %>% 
  filter(year==2007) %>% # First off, we filter the data, so it only includes data from the year 2007
  group_by(country) %>% # We then group the data by country, since it is the five richest *countries* we need to find
  summarize(gdpPercap) %>% # Summarize by gdpPercap, since it is the five *richest* countries we need to find
  arrange(desc(gdpPercap)) %>% # Arranging the countries, so it is the richest to poorest
  head(n = 5) # Taking the top five countries, which would be the five richest countries
## # A tibble: 5 × 2
##   country       gdpPercap
##   <fct>             <dbl>
## 1 Norway           49357.
## 2 Kuwait           47307.
## 3 Singapore        47143.
## 4 United States    42952.
## 5 Ireland          40676.

The five richest countries in the world in 2007, in an ascending order, is: Norway, Kuwait, Singapore, United States and Ireland

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

  1. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

  2. Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.

  • #5 & 6:
anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() + # We want to represent the data in a scatterplot
  scale_x_log10() + # convert x to log scale
  transition_time(year)+ # Make the animation
  enter_fade() + 
  exit_shrink() +
  ease_aes('linear') + 
  labs(title = 'Year: {round(frame_time,0)}') + # Adding a title that is sync with the animation
  ylab("Life expectancy (years)") + # Adding some nice axes labels
  xlab("GDP per capita (constant 2011 international $)") + 
  labs(color = "Continent", size = "Population size") + # Make the legend labels nice as well
  scale_y_continuous(n.breaks = 15) # Adding some extra ticks on the y-axis, to make it more legible
anim3

  1. Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]
  • #7: I want to see the mean life expectancy across continents over the years, as to see how the mean life expectancy develops for the different continents.
# We start off with altering the data set, so it fits our research question
my_gap = gapminder %>%  
  group_by(continent,year) %>% # We want to make the mean for all continents for all years
  summarize(mean(lifeExp)) # And here is where we take the actual mean
## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.
colnames(my_gap)[3] = "mean_life_exp" # Rename the column, so we can work with it more easily

skrrt_animate = ggplot(my_gap, aes(continent,mean_life_exp, fill = continent))+ # We set up the animation, make it so that the continents are on the x-axis and mean life expectancies are on the y-axis
  geom_col() + # We want the data represented by columns
  transition_time(year) + # Making the animation
  labs(title = 'Year: {round(frame_time,0)}') + # Adding a title that is in sync with the animation 
  ylab("Mean Life expectancy (years)") + # Adding some more nice labels for the axes
  xlab("Continent") +
  theme(legend.position="none") # The legend is redundant, so we remove it (it just denotes the same stuff that the x-axis does)
skrrt_animate

My data visualization answers the question in terms of that it illustrates the development of the mean life expectancies of the continents over the years. We see that the general trend for the continents, is that the mean life expectancy steadily increases for them all - the difference is that some countries develop much faster, such Asia, whereas the one for Africa actually stagnates around year 1990-2000.